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set nproc_per_node as var in speedrun and run1000 scripts

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svlandeg 2025-11-04 21:36:10 +01:00
parent d1558c7873
commit f1683c5b16
2 changed files with 23 additions and 16 deletions
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18
run1000.sh
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@ -70,18 +70,22 @@ python -m scripts.tok_eval
# which would decrease model performance. Possibly 2, 3 or so epochs is ~ok, but certainly not ideal and at 10+ epochs we'd # which would decrease model performance. Possibly 2, 3 or so epochs is ~ok, but certainly not ideal and at 10+ epochs we'd
# start to overfit hard. # start to overfit hard.
# 5) That's it, everything else (e.g. the learning rates) is adjusted automatically by the training script. # 5) That's it, everything else (e.g. the learning rates) is adjusted automatically by the training script.
torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=32 --device_batch_size=8 --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=8 -m scripts.base_loss # Number of processes/GPUs to use
torchrun --standalone --nproc_per_node=8 -m scripts.base_eval NPROC_PER_NODE=8
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- --depth=32 --device_batch_size=8 --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_loss
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_eval
# midtrain # midtrain
# NOTE: ensure that we use the same device_batch_size here as the base training script. # NOTE: ensure that we use the same device_batch_size here as the base training script.
torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --device_batch_size=8 --run=$WANDB_RUN torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.mid_train -- --device_batch_size=8 --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i mid torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_eval -- -i mid
# sft # sft
torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft -- --run=$WANDB_RUN torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_sft -- --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_eval -- -i sft
# generate final report # generate final report
python -m nanochat.report generate python -m nanochat.report generate

21
speedrun.sh
View File

@ -82,12 +82,15 @@ python -m scripts.tok_eval
echo "Waiting for dataset download to complete..." echo "Waiting for dataset download to complete..."
wait $DATASET_DOWNLOAD_PID wait $DATASET_DOWNLOAD_PID
# Number of processes/GPUs to use
NPROC_PER_NODE=8
# pretrain the d20 model # pretrain the d20 model
torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=20 --run=$WANDB_RUN torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- --depth=20 --run=$WANDB_RUN
# evaluate the model on a larger chunk of train/val data and draw some samples # evaluate the model on a larger chunk of train/val data and draw some samples
torchrun --standalone --nproc_per_node=8 -m scripts.base_loss torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_loss
# evaluate the model on CORE tasks # evaluate the model on CORE tasks
torchrun --standalone --nproc_per_node=8 -m scripts.base_eval torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_eval
# ----------------------------------------------------------------------------- # -----------------------------------------------------------------------------
# Midtraining (teach the model conversation special tokens, tool use, multiple choice) # Midtraining (teach the model conversation special tokens, tool use, multiple choice)
@ -97,15 +100,15 @@ torchrun --standalone --nproc_per_node=8 -m scripts.base_eval
curl -L -o $NANOCHAT_BASE_DIR/identity_conversations.jsonl https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl curl -L -o $NANOCHAT_BASE_DIR/identity_conversations.jsonl https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl
# run midtraining and eval the model # run midtraining and eval the model
torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --run=$WANDB_RUN torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.mid_train -- --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i mid torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_eval -- -i mid
# ----------------------------------------------------------------------------- # -----------------------------------------------------------------------------
# Supervised Finetuning (domain adaptation to each sequence all by itself per row) # Supervised Finetuning (domain adaptation to each sequence all by itself per row)
# train sft and re-eval right away (should see a small bump) # train sft and re-eval right away (should see a small bump)
torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft -- --run=$WANDB_RUN torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_sft -- --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_eval -- -i sft
# chat with the model over CLI! Leave out the -p to chat interactively # chat with the model over CLI! Leave out the -p to chat interactively
# python -m scripts.chat_cli -p "Why is the sky blue?" # python -m scripts.chat_cli -p "Why is the sky blue?"
@ -118,9 +121,9 @@ torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft
# (optional) # (optional)
# run reinforcement learning # run reinforcement learning
# torchrun --standalone --nproc_per_node=8 -m scripts.chat_rl -- --run=$WANDB_RUN # torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_rl -- --run=$WANDB_RUN
# eval the RL model only on GSM8K # eval the RL model only on GSM8K
# torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i rl -a GSM8K # torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_eval -- -i rl -a GSM8K
# ----------------------------------------------------------------------------- # -----------------------------------------------------------------------------
# Generate the full report by putting together all the sections # Generate the full report by putting together all the sections